Decoding the information in the primary sequence of a protein is one of the most fundamental challenges in modern biology. A protein's sequence encodes more than just the native structure; it encodes the entire energy landscape - an ensemble of conformations whose energetics and dynamics are finely tuned. The goal of this proposal is a molecular, quantitative, and predictive understanding of the relationship between sequence and the energy landscape together with an understanding of how the environment modulates this landscape. A major hurdle in going from sequence to function is our lack of understanding of the non-native or high- energy regions of the landscape and how they are modulated by the environment. High-energy conformations are important for directing the stability and folding of a protein, and modulations of this ensemble play a role in misfolding, protein signaling, catalytic activity, and allostery. While, many sequences can encode the same structure, their function and dynamics can vary dramatically - due to changes in the landscape. Small variations in a sequence can have effects that range from undetectable to pathological. Soon we will have access to thousands of human genomes, and without our ability to interpret variation, the potential of these data to impact medicine and human health will never be fully appreciated. It is imperative, therefore, that we have an understanding and control over the relationship between sequence and the energy landscape. Modulations of the energy landscape are not easily detected due to the small populations and transient nature of the high-energy species. The experiments outlined here are aimed at understanding how changes in the sequence and the environment affect the energy landscape. Aim 1: Quantitative measures of protein folding and stability in complex environments a. Develop a quantitative bench-top measure of protein stability on the ribosome and other complex mixtures. b. Measure conformational dynamics of ribosome-bound polypeptide chains c. Monitor translational coupled folding using HaloTag as a model system Aim 2: Probe the energy landscape through evolution and sequence modulation a. Use Ancestral Sequence Reconstruction (ASR) to explore changes in the landscape of RNase H over time. We will evaluate the energy landscapes of these resurrected proteins to determine how optimizations of the energy landscape, and thus function/fitness, occur over evolutionary time. b. Use Ancestral Sequence Reconstruction to evaluate the rate-limiting step by using the alpha-lytic protease family of both kinetically stable and thermodynamically stable proteases Aim 3: Probe the energy landscape through single molecule mechanical unfolding a. Single molecule mechanical studies to probe the energy barriers in protein folding